图 书 馆
一种基于交替方向法的矫正联合稀疏向量的快速算法A Fast Algorithm for Recovery of Jointly Sparse Vectors based on the Alternating Direction Methods |
|
| 课程网址: | http://videolectures.net/aistats2011_lu_algorithm/ |
| 主讲教师: | Hongtao Lu |
| 开课单位: | 上海交通大学 |
| 开课时间: | 2011-05-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The standard compressive sensing (CS) aims to recover sparse signal from single measurement vector which is known as SMV model. By contrast, recovery of sparse signals from multiple measurement vectors is called MMV model. In this paper, we consider the recovery of jointly sparse signals in the MMV model where multiple signal measurements are represented as a matrix and the sparsity of signal occurs in common locations. The sparse MMV model can be formulated as a matrix (2; 1)-norm minimization problem, which is much more difficult to solve than the l1-norm minimization in standard CS. In this paper, we propose a very fast algorithm, called MMV-ADM, to solve the jointly sparse signal recovery problem in MMV settings based on the alternating direction method (ADM). The MMV-ADM alternately updates the recovered signal matrix, the Lagrangian multiplier and the residue, and all update rules only involve matrix or vector multiplications and summations, so it is simple, easy to implement and much faster than the state-of-the-art method MMVprox. Numerical simulations show that MMV-ADM is at least dozens of times faster than MMVprox with comparable recovery accuracy. |
| 关 键 词: | 矩阵; 标准压缩感知; 稀疏信号; 交替方向法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-27：lxf |
| 阅读次数: | 56 |